{"id":"W2929273575","doi":"10.48550/arxiv.1904.01031","title":"Modular Synthesis of Divide-and-Conquer Parallelism for Nested Loops (Extended Version)","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Nested loop join; Parallel computing; Loop fusion; Divide and conquer algorithms; Loop tiling; Correctness; Modular design; Loop (graph theory); Abstraction; Parallelism (grammar); Loop fission; Programming language; Traverse; Automatic parallelization; Theoretical computer science; Compiler; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003370653,0.0003010278,0.0005138083,0.0003061132,0.000118706,0.00007161163,0.001425977,0.0003330278,0.000008624828],"category_scores_gemma":[0.0001165113,0.0003376767,0.0002394776,0.0002903965,0.0001026668,0.000260171,0.001570056,0.0002508121,0.00001389623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007145779,"about_ca_system_score_gemma":0.0001416726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007665016,"about_ca_topic_score_gemma":0.000002131151,"domain_scores_codex":[0.9982253,0.0001424147,0.0002750949,0.0009686003,0.000101301,0.0002872635],"domain_scores_gemma":[0.9976538,0.0003885381,0.0003929369,0.001142573,0.0003108849,0.0001112072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006675324,0.0001173715,0.001065872,0.000307514,0.0001736365,0.00002498149,0.0001497351,0.8688828,0.00006059956,0.1264383,0.001018614,0.001693825],"study_design_scores_gemma":[0.0004292938,0.00006362772,0.001260957,0.0001798357,0.00008874879,0.000002564915,0.00001236007,0.9614902,0.001021268,0.03448091,0.0005637356,0.0004065756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04374494,0.00009957249,0.9540817,0.0001427256,0.0002823466,0.0005251765,0.00002013191,0.0003411814,0.000762217],"genre_scores_gemma":[0.9655685,0.0002648154,0.03309281,0.0000689267,0.00002813406,0.000003664376,0.00001726859,0.00001950149,0.0009363649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9218236,"threshold_uncertainty_score":0.9999076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04397603319946865,"score_gpt":0.1957794254449125,"score_spread":0.1518033922454438,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}